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神经象征数字双胞胎用于心血管疾病预测和个性化建模

Muhammad Adnan, Yang Yi, Niyaz Ahmad Wani

IEEE journal of biomedical and health informatics

|December 23, 2025

概括

NeuroTwin是一种新的神经符号数字双胞胎,通过集成先进的AI模块来进行精确的诊断和个性化治疗计划,从而增强心血管保健. 它实现了高精度,同时确保了患者的隐私和因果解释性.

科学领域:

生物医学工程生物医学工程
人工智能在医学中的应用
计算心脏病学计算心脏病学

背景情况:

心血管疾病管理需要准确的诊断,因果理解,个性化治疗和数据隐私.
现有的临床决策框架往往缺乏整合这些关键组成部分.

研究的目的:

介绍NeuroTwin,一个神经符号数字双胞胎,旨在统一心血管临床决策.
评估NeuroTwin在诊断精度,治疗优化,因果解释性和隐私保护方面的有效性.

主要方法:

NeuroTwin集成了四个模块:适应性扩散变压器 (ADViT) 用于信号消音和融合,象征性因果发现网络 (SCDN) 用于因果图构建,神经联合数字双胞胎 (NFDT) 用于私有分布式学习,以及层次的元强化学习器 (HMRL) 用于治疗建议.
ADViT使用补丁级编码和ECG/PCG信号的交叉模式融合.
对于规则生成,SCDN使用可微分的循环性约束.
NFDT使用差异私有高斯聚合来进行联合学习.
HMRL实施双层政策,以优化治疗.

主要成果:

神经双胞胎证明了98.5%的诊断精度和96.2%的治疗优化成功率.
获得了0.942的因果解释性得分.
该系统保持低隐私泄露率,为0.032.

结论:

NeuroTwin为先进的心血管预测和治疗规划提供了一个强大的,集成的框架.
该系统有效地平衡了诊断准确性,治疗个性化,因果透明度和数据隐私.
NeuroTwin代表了人工智能驱动的心血管护理临床决策支持的重大进展.

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